Recently, Tower Chief Investment Officer John Cogman joined a panel discussion at Citi Quant Services’ Executive Leadership Forum. The topic: the evolution of alpha, liquidity, and trading horizons.
The conversation explored the growing convergence of high-frequency trading (HFT) and medium-frequency trading (MFT), changing approaches to liquidity, the influence of retail participation and emerging market structures, and the intensifying competition for quantitative and AI talent.
The Convergence of HFT and MFT
The boundaries between HFT and MFT have become increasingly fluid as firms expand across trading horizons.
John noted that Tower has been building beyond its traditional HFT capabilities for more than a decade. The shift reflects several factors, including technology synergies across strategies, the opportunity to attract a wider range of quantitative talent, and margin compression within HFT.
Longer holding periods, however, introduce different risk and portfolio management requirements. While Tower already uses sophisticated systems to monitor inventory and exposures within its HFT business, MFT strategies require firms to recalibrate factor models, risk systems, and portfolio monitoring tools.
They also demand patience. Strategies with lower Sharpe ratios or longer horizons require more time to determine whether an alpha signal is genuinely effective. Firms must balance that longer evaluation period with disciplined risk management and clear performance standards.
Evolving Approaches to Liquidity and Market Interaction
As markets become more fragmented, firms are adapting how they access, price, and interact with liquidity.
The industry has seen an uptick in bilateral trading among electronic liquidity providers, and John highlighted this as an important market development that quantitative trading firms need to understand. He framed Tower’s participation primarily as defensive, helping the firm remain competitive as price discovery takes place across a growing number of venues.
The same measured approach applies to block trading. John stated that Tower is generally more comfortable pricing derivative-based instruments than single-name equities. This reflects the importance of understanding both the instrument and the nature of the order flow before committing capital.
New Forces Reshaping Markets and Investment Priorities
Retail participation has evolved from a peripheral force into a structural feature of modern markets. John noted that while retail activity improves liquidity overall, meme stocks and similar phenomena demonstrate how quickly market behavior can shift in ways that historical models may not capture.
The rise of retail has demanded new datasets and a cautious, systematic approach to model development. As retail investors gain access to more sophisticated tools and AI-driven insights, their influence on price formation and cross-sectional market behavior is likely to continue growing.
This is hardly the only market evolution forcing firms to adapt. The proliferation of 24-hour markets and tokenized products is creating new infrastructure demands. Firms must prepare for more continuous price discovery and invest in the systems required to participate across evolving venues and market structures. For Tower, relevant priorities include AI, compute capacity, quantitative talent, market connectivity, and trading infrastructure.
Competition for Quantitative and AI Talent
The talent market is evolving as quickly as the technology it supports.
Demand for machine learning and deep learning expertise has intensified, both within and outside of finance. Trading firms are no longer competing only with one another – they are also competing with AI labs and hyperscalers for a limited pool of highly specialized researchers and engineers.
For quantitative trading firms, success will increasingly depend on their ability to combine strong research environments, scalable infrastructure, and opportunities to apply advanced technical skills to complex real-world markets.
The discussion reinforced a broader shift across quantitative trading. Competitive advantage is not defined by speed alone. It increasingly depends on how effectively firms bring together alpha research, execution quality, risk management, infrastructure, and talent across multiple trading horizons. Tower values opportunities to exchange ideas with industry peers and to continue sharing insights on the trends shaping quantitative markets.